243 research outputs found
A Fast General-Purpose Clustering Algorithm Based on FPGAs for High-Throughput Data Processing
We present a fast general-purpose algorithm for high-throughput clustering of
data "with a two dimensional organization". The algorithm is designed to be
implemented with FPGAs or custom electronics. The key feature is a processing
time that scales linearly with the amount of data to be processed. This means
that clustering can be performed in pipeline with the readout, without
suffering from combinatorial delays due to looping multiple times through all
the data. This feature makes this algorithm especially well suited for problems
where the data has high density, e.g. in the case of tracking devices working
under high-luminosity condition such as those of LHC or Super-LHC. The
algorithm is organized in two steps: the first step (core) clusters the data;
the second step analyzes each cluster of data to extract the desired
information. The current algorithm is developed as a clustering device for
modern high-energy physics pixel detectors. However, the algorithm has much
broader field of applications. In fact, its core does not specifically rely on
the kind of data or detector it is working for, while the second step can and
should be tailored for a given application. Applications can thus be foreseen
to other detectors and other scientific fields ranging from HEP calorimeters to
medical imaging. An additional advantage of this two steps approach is that the
typical clustering related calculations (second step) are separated from the
combinatorial complications of clustering. This separation simplifies the
design of the second step and it enables it to perform sophisticated
calculations achieving online-quality in online applications. The algorithm is
general purpose in the sense that only minimal assumptions on the kind of
clustering to be performed are made.Comment: 11th Frontier Detectors For Frontier Physics conference (2009
An automated system for lung nodule detection in low-dose computed tomography
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical Computed Tomography (CT) images was
developed in the framework of the MAGIC-5 Italian project. One of the main
goals of this project is to build a distributed database of lung CT scans in
order to enable automated image analysis through a data and cpu GRID
infrastructure. The basic modules of our lung-CAD system, a dot-enhancement
filter for nodule candidate selection and a neural classifier for
false-positive finding reduction, are described. The system was designed and
tested for both internal and sub-pleural nodules. The results obtained on the
collected database of low-dose thin-slice CT scans are shown in terms of free
response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging
Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514,
65143
An automatic system to discriminate malignant from benign massive lesions in mammograms
Evaluating the degree of malignancy of a massive lesion on the basis of the
mere visual analysis of the mammogram is a non-trivial task. We developed a
semi-automated system for massive-lesion characterization with the aim to
support the radiological diagnosis. A dataset of 226 masses has been used in
the present analysis. The system performances have been evaluated in terms of
the area under the ROC curve, obtaining A_z=0.80+-0.04.Comment: 4 pages, 2 figure; Proceedings of the Frontier Science 2005, 4th
International Conference on Frontier Science, 12-17 September, 2005, Milano,
Ital
Computer-aided detection of pulmonary nodules in low-dose CT
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical CT images with 1.25 mm slice
thickness is being developed in the framework of the INFN-supported MAGIC-5
Italian project. The basic modules of our lung-CAD system, a dot enhancement
filter for nodule candidate selection and a voxel-based neural classifier for
false-positive finding reduction, are described. Preliminary results obtained
on the so-far collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the CompIMAGE - International
Symposium on Computational Modelling of Objects Represented in Images:
Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portuga
A scalable system for microcalcification cluster automated detection in a distributed mammographic database
A computer-aided detection (CADe) system for microcalcification cluster
identification in mammograms has been developed in the framework of the
EU-founded MammoGrid project. The CADe software is mainly based on wavelet
transforms and artificial neural networks. It is able to identify
microcalcifications in different datasets of mammograms (i.e. acquired with
different machines and settings, digitized with different pitch and bit depth
or direct digital ones). The CADe can be remotely run from GRID-connected
acquisition and annotation stations, supporting clinicians from geographically
distant locations in the interpretation of mammographic data. We report and
discuss the system performances on different datasets of mammograms and the
status of the GRID-enabled CADe analysis.Comment: 6 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference,
October 23-29, 2005, Puerto Ric
A Theoretical Prediction of the Bs-Meson Lifetime Difference
We present the results of a quenched lattice calculation of the operator
matrix elements relevant for predicting the Bs width difference. Our main
result is (\Delta\Gamma_Bs/\Gamma_Bs)= (4.7 +/- 1.5 +/- 1.6) 10^(-2), obtained
from the ratio of matrix elements, R(m_b)=/<\bar
B_s^0|Q_L|B_s^0>=-0.93(3)^(+0.00)_(-0.01). R(m_b) was evaluated from the two
relevant B-parameters, B_S^{MSbar}(m_b)=0.86(2)^(+0.02)_(-0.03) and
B_Bs^{MSbar}(m_b) = 0.91(3)^(+0.00)_(-0.06), which we computed in our
simulation.Comment: 21 pages, 7 PostScript figure
A scalable Computer-Aided Detection system for microcalcification cluster identification in a pan-European distributed database of mammograms
A computer-aided detection (CADe) system for microcalcification cluster
identification in mammograms has been developed in the framework of the
EU-founded MammoGrid project. The CADe software is mainly based on wavelet
transforms and artificial neural networks. It is able to identify
microcalcifications in different kinds of mammograms (i.e. acquired with
different machines and settings, digitized with different pitch and bit depth
or direct digital ones). The CADe can be remotely run from GRID-connected
acquisition and annotation stations, supporting clinicians from geographically
distant locations in the interpretation of mammographic data. We report the
FROC analyses of the CADe system performances on three different dataset of
mammograms, i.e. images of the CALMA INFN-founded database collected in the
Italian National screening program, the MIAS database and the so-far collected
MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false
positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im
have been obtained on the CALMA, MIAS and MammoGrid database respectively.Comment: 6 pages, 5 figures; Proceedings of the ITBS 2005, 3rd International
Conference on Imaging Technologies in Biomedical Sciences, 25-28 September
2005, Milos Island, Greec
A Computer-Aided Detection system for lung nodules in CT images
Lung cancer is the leading cause of cancer-related mortality in developed countries. To support radiologists in the identification of early-stage lung cancers, we propose a Computer-Aided Detection (CAD) system, composed
by two different procedures: VBNACADI devoted to the identification of small nodules embedded in the lung parenchyma (internal nodules) and VBNACADJP devoted the identification of nodules originating on the pleura surface (juxta-pleural nodules). The CAD system has been developed and tested on a dataset of low-dose and thin-slice CT scans collected in the framework of the first Italian randomized and controlled screening trial (ITALUNG-CT). This work has been carried out in the framework of MAGIC-5 (Medical Application on a Grid Infrastructure Connection), an Italian collaboration funded by Istituto Nazionale di Fisica Nucleare (INFN) and Ministero dell’Universit`a e della Ricerca (MIUR), which aims at developing models and algorithms for a distributed analysis of biomedical images, by making use of the GRID services
Effect of data harmonization of multicentric dataset in ASD/TD classification
Machine Learning (ML) is nowadays an essential tool in the analysis of Magnetic Resonance Imaging (MRI) data, in particular in the identification of brain correlates in neurological and neurodevelopmental disorders. ML requires datasets of appropriate size for training, which in neuroimaging are typically obtained collecting data from multiple acquisition centers. However, analyzing large multicentric datasets can introduce bias due to differences between acquisition centers. ComBat harmonization is commonly used to address batch effects, but it can lead to data leakage when the entire dataset is used to estimate model parameters. In this study, structural and functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) collection were used to classify subjects with Autism Spectrum Disorders (ASD) compared to Typical Developing controls (TD). We compared the classical approach (external harmonization) in which harmonization is performed before train/test split, with an harmonization calculated only on the train set (internal harmonization), and with the dataset with no harmonization. The results showed that harmonization using the whole dataset achieved higher discrimination performance, while non-harmonized data and harmonization using only the train set showed similar results, for both structural and connectivity features. We also showed that the higher performances of the external harmonization are not due to larger size of the sample for the estimation of the model and hence these improved performance with the entire dataset may be ascribed to data leakage. In order to prevent this leakage, it is recommended to define the harmonization model solely using the train set
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